121 lines
5.1 KiB
C++
121 lines
5.1 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2020 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#ifndef TIM_LAYOUT_INFER_ACTIVATION_LAYOUT_INFERENCE_H_
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#define TIM_LAYOUT_INFER_ACTIVATION_LAYOUT_INFERENCE_H_
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#include "tim/vx/ops/activations.h"
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#include "ops/op_layout_inference.h"
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#include "permute_vector.h"
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#include "direct_map_op_impl.h"
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namespace tim {
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namespace transform {
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template <typename OpType>
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class ActivationLayoutInfer : public OpLayoutInfer {
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public:
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ActivationLayoutInfer(
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const std::shared_ptr<vx::Operation> op,
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std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
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: OpLayoutInfer(op, context) {}
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void OnInputs(
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std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
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// Transmit input pv to out pv directly for activation ops
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assert(op_->impl()->InputsTensor().size() == 1);
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auto i_src = op_->impl()->InputsTensor()[0];
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auto input_pv = context_->GetPermuteVector(i_src);
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auto activation = context_->infer_graph_->CreateOperation<OpType>();
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auto out_infer = CreateOutputsTensor(input_pv);
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(*activation)
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.BindInput(context_->GetMapedTensor(i_src))
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.BindOutput(out_infer[0]);
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context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], input_pv);
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next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
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}
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};
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class LeakyReluLayoutInfer : public OpLayoutInfer {
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public:
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LeakyReluLayoutInfer(
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const std::shared_ptr<vx::Operation> op,
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std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
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: OpLayoutInfer(op, context) {}
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void OnInputs(
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std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
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assert(op_->impl()->InputsTensor().size() == 1);
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auto i_src = op_->impl()->InputsTensor()[0];
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auto input_pv = context_->GetPermuteVector(i_src);
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auto leaky_relu =
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context_->infer_graph_->CreateOperation<vx::ops::LeakyRelu>(
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op_->impl()->node()->nn_param.activation.leaky_ratio);
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auto out_infer = CreateOutputsTensor(input_pv);
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(*leaky_relu)
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.BindInput(context_->GetMapedTensor(i_src))
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.BindOutput(out_infer[0]);
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context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], input_pv);
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next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
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}
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};
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class PReluLayoutInfer : public OpLayoutInfer {
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public:
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PReluLayoutInfer(
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const std::shared_ptr<vx::Operation> op,
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std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
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: OpLayoutInfer(op, context) {}
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void OnInputs(
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std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
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ReverseInputsPermuteVector();
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auto src_input = op_->impl()->InputsTensor()[0];
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auto input_pv = context_->GetPermuteVector(src_input);
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auto prelu = context_->infer_graph_->CreateOperation<vx::ops::Prelu>(
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op_->impl()->node()->nn_param.prelu.axis);
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auto out_infer = CreateOutputsTensor(input_pv);
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for (const auto& i_src : op_->impl()->InputsTensor()) {
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(*prelu).BindInput(context_->GetMapedTensor(i_src));
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}
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(*prelu).BindOutput(out_infer[0]);
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context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], input_pv);
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next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
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}
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};
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using ReluLayoutInfer = ActivationLayoutInfer<vx::ops::Relu>;
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using Relu1LayoutInfer = ActivationLayoutInfer<vx::ops::Relu1>;
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using Relu6LayoutInfer = ActivationLayoutInfer<vx::ops::Relu6>;
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using EluLayoutInfer = ActivationLayoutInfer<vx::ops::Elu>;
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using SigmoidLayoutInfer = ActivationLayoutInfer<vx::ops::Sigmoid>;
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using MishLayoutInfer = ActivationLayoutInfer<vx::ops::Mish>;
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using HardSigmoidLayoutInfer = ActivationLayoutInfer<vx::ops::HardSigmoid>;
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using SoftReluLayoutInfer = ActivationLayoutInfer<vx::ops::SoftRelu>;
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using HardSwishLayoutInfer = ActivationLayoutInfer<vx::ops::HardSwish>;
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using TanhLayoutInfer = ActivationLayoutInfer<vx::ops::Tanh>;
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} // namespace transform
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} // namespace tim
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#endif |